There is no shortage of marketing buzzwords with short-term industry hype. When I first heard about the intersection of AI and marketing, while working with a machine-learning startup out of the Bay Area, I assumed the use of bots in marketing would follow suit as a buzzworthy concept that would quickly run its course. Fast-forward five years, and it's evident that AI and its related technologies will run parallel to nearly all marketing tactics in the future. Here are three unexpected ways that the adoption of AI is currently shifting and will continue to accelerate the evolution of the marketing landscape over the next three to five years. Perhaps the greatest potential benefit of AI within the context of marketing, sales, customer support and business in general is the capacity for it to reduce what is known to psychologists as "Cognitive load."
Understanding an emotion isn't as simple as noticing a smile-- but we still look to facial movements for everything from navigating everyday social interactions to the development of emotionally attuned artificial intelligence. According to a July 2019 study from researchers at Northeastern and the California Institute of Technology, facial expressions only reflect the surface of emotions: The culture, situation, and specific individual around a facial expression add nuance to the way a feeling is conveyed. For example, the researchers note that Olympic athletes who won medals only smiled when they knew they were being watched by an audience. While they were waiting behind the podium or facing away from people, they didn't smile (but were probably still happy). These results reinforce the idea that facial expressions aren't always reliable indicators of emotion.
"Astro" the robodog promises many things, but its face is the stuff of nightmares. Researchers from Florida Atlantic University (FAU) set out to build a robodog that combined all the best parts of Siri, 3D-printing, and nimble, next-gen robots like Boston Dynamics' SpotMini. Unfortunately, Astro's engineers added an extra component to the bot: horrible, human-like eyes. Housed inside Astro's 3D-printed, Doberman pinscher-like head is a computer system that uses deep learning to "learn from experience to perform human-like tasks, or in his case, 'doggie-like' tasks, that benefit humanity." Astro and a handful of other robots like it are still "pupp[ies]-in-training" but currently respond to commands like "sit," "stand," and "lie down."
Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible. Common NLP use cases include the following: reading documents and identifying errors for support activities such as information verification; user identification, and approvals; improving the underwriting process and capital efficiency; understanding customer queries via voice search on digital voice assistants or smartphones. Deloitte found the financial services firms participating in their study's most common use cases for machine learning include the following. Predicting cash-flow events and proactively advising customers on spending and saving habits; expanding the data set for developing credit scores and applying machine learning to build advanced credit models for expanding reach and reducing defaults; providing machine-learning-based merchant analytics "as a service"; and detecting patterns in transactions and identifying fraudulent transactions as early as possible.
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Nvidia CEO Jensen Huang said AI would drive long-term demand because it is the "single most powerful force of our time." Nvidia reported earnings and revenues that beat analysts' expectations as demand for graphics and artificial intelligence chips picked up in the second fiscal quarter. Huang also said his company's near-term growth will come from gaming and a couple of variants of the company's artificial intelligence chip business: inferencing and AI at the edge. During a conference call with analysts, Huang said artificial intelligence is the "single most powerful force of our time" and that there are more than 4,000 AI startups working with the company -- as compared to 2,000 AI startups in April 2017. In an interview with VentureBeat, Huang said the actual number of AI startups Nvidia is tracking is closer to 4,500.
Living in the Seattle area, I have the opportunity to be exposed to the latest and greatest artificial intelligence (AI) experiences, like the Amazon Go Store, which knows when you pick up an item in the store for checkout and when you put one back, all culminating in an app that simplifies your checkout experience with automation. These are the types of AI experiences that businesses hope for and can attain if they harness AI not only for the rewards, but also with an eye on managing the risks and ensuring their own readiness. SEE: The ethical challenges of AI: A leader's guide (free PDF) (TechRepublic) Alex Fly, CEO of AI solution provider Quickpath, calls this the "three Rs" of artificial intelligence: Reward, risk, and readiness. "What CIOs and other individuals at the C-level [in organizations] should note is that AI is a methodology that uses an experimental framework," said Fly. When you implement AI, whether it is operating on big data, traditional data, or a blend of the two, the testing process is iterative.
WIRED recently highlighted unacceptable levels of bias in facial recognition in the article The Best Algorithms Struggle to Recognize Black Faces Equally. They cited the poor test scores of leading facial recognition vendors, as reported by the National Institute of Standards and Technology (NIST) in its July 2019 results. WIRED specifically called out Idemia but generalized their concerns. "The NIST test challenged algorithms to verify that two photos showed the same face, similar to how a border agent would check passports. At sensitivity settings where Idemia's algorithms falsely matched different white women's faces at a rate of one in 10,000, it falsely matched black women's faces about once in 1,000 -- 10 times more frequently. A one in 10,000 false match rate is often used to evaluate facial recognition systems."
Russia's space agency Roscosmos is about to send a humanoid robot to the International Space Station. Skybot F-850 will be sent to the ISS on August 22 on board the Soyuz MS-14 spacecraft, and will spend over two weeks there before returning to Earth on September 7. The robot, also known as Fedor, made headlines in 2017 when Dmitry Rogozin, director general of Roscosmos, shared a video of it shooting guns. Shortly after he clarified they "are not creating a Terminator, but artificial intelligence that will be of great practical significance in various fields." Fedor was created to replicate the movement of a remote operator.